Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells73
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory129.3 B

Variable types

Numeric13
Categorical3

Alerts

id has a high cardinality: 100 distinct values High cardinality
name has a high cardinality: 100 distinct values High cardinality
symbol has a high cardinality: 100 distinct values High cardinality
Unnamed: 0 is highly correlated with id and 3 other fieldsHigh correlation
24h_volume_usd is highly correlated with id and 7 other fieldsHigh correlation
available_supply is highly correlated with id and 6 other fieldsHigh correlation
market_cap_usd is highly correlated with 24h_volume_usd and 6 other fieldsHigh correlation
max_supply is highly correlated with available_supply and 5 other fieldsHigh correlation
percent_change_7d is highly correlated with 24h_volume_usd and 6 other fieldsHigh correlation
price_btc is highly correlated with 24h_volume_usd and 6 other fieldsHigh correlation
price_usd is highly correlated with 24h_volume_usd and 6 other fieldsHigh correlation
rank is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
total_supply is highly correlated with available_supply and 6 other fieldsHigh correlation
percent_change_24h is highly correlated with available_supply and 7 other fieldsHigh correlation
name is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
symbol is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
id is highly correlated with Unnamed: 0 and 14 other fieldsHigh correlation
last_updated is highly correlated with 24h_volume_usd and 6 other fieldsHigh correlation
percent_change_1h is highly correlated with id and 3 other fieldsHigh correlation
max_supply has 73 (73.0%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
id is uniformly distributed Uniform
name is uniformly distributed Uniform
rank is uniformly distributed Uniform
symbol is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
24h_volume_usd has unique values Unique
id has unique values Unique
market_cap_usd has unique values Unique
name has unique values Unique
percent_change_24h has unique values Unique
price_btc has unique values Unique
price_usd has unique values Unique
rank has unique values Unique
symbol has unique values Unique

Reproduction

Analysis started2022-10-23 13:30:57.709194
Analysis finished2022-10-23 13:31:29.122526
Duration31.41 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.5
Minimum0
Maximum99
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:29.372018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.95
Q124.75
median49.5
Q374.25
95-th percentile94.05
Maximum99
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.01149198
Coefficient of variation (CV)0.586090747
Kurtosis-1.2
Mean49.5
Median Absolute Deviation (MAD)25
Skewness0
Sum4950
Variance841.6666667
MonotonicityStrictly increasing
2022-10-23T16:31:29.593637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
1.0%
631
 
1.0%
731
 
1.0%
721
 
1.0%
711
 
1.0%
701
 
1.0%
691
 
1.0%
681
 
1.0%
671
 
1.0%
661
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
01
1.0%
11
1.0%
21
1.0%
31
1.0%
41
1.0%
51
1.0%
61
1.0%
71
1.0%
81
1.0%
91
1.0%
ValueCountFrequency (%)
991
1.0%
981
1.0%
971
1.0%
961
1.0%
951
1.0%
941
1.0%
931
1.0%
921
1.0%
911
1.0%
901
1.0%

24h_volume_usd
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531306623.4
Minimum389519
Maximum2.20813 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:29.796290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum389519
5-th percentile3167867.5
Q119833325
median44427950
Q3171382750
95-th percentile2148558500
Maximum2.20813 × 1010
Range2.208091048 × 1010
Interquartile range (IQR)151549425

Descriptive statistics

Standard deviation2354402555
Coefficient of variation (CV)4.431344258
Kurtosis72.68427331
Mean531306623.4
Median Absolute Deviation (MAD)32897200
Skewness8.115844267
Sum5.313066234 × 1010
Variance5.543211389 × 1018
MonotonicityNot monotonic
2022-10-23T16:31:29.981706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.20813 × 10101
 
1.0%
310261001
 
1.0%
284592001
 
1.0%
141476001
 
1.0%
1696700001
 
1.0%
1364250001
 
1.0%
39644701
 
1.0%
321259001
 
1.0%
637787001
 
1.0%
30424201
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
3895191
1.0%
5168071
1.0%
26507201
1.0%
30067401
1.0%
30424201
1.0%
31744701
1.0%
38568101
1.0%
39644701
1.0%
57120701
1.0%
79044801
1.0%
ValueCountFrequency (%)
2.20813 × 10101
1.0%
57056900001
1.0%
52213700001
1.0%
30321500001
1.0%
29716100001
1.0%
21052400001
1.0%
15699000001
1.0%
14359300001
1.0%
6563890001
1.0%
6476940001
1.0%

available_supply
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3975344 × 1010
Minimum645222
Maximum2.509983876 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:30.153111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum645222
5-th percentile2948346.7
Q154789875.5
median204527322
Q31502972959
95-th percentile1.161786859 × 1011
Maximum2.509983876 × 1012
Range2.509983231 × 1012
Interquartile range (IQR)1448183084

Descriptive statistics

Standard deviation2.636905979 × 1011
Coefficient of variation (CV)5.996328258
Kurtosis79.5844835
Mean4.3975344 × 1010
Median Absolute Deviation (MAD)195585980
Skewness8.646040496
Sum4.3975344 × 1012
Variance6.95327314 × 1022
MonotonicityNot monotonic
2022-10-23T16:31:30.440474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500000002
 
2.0%
167852251
 
1.0%
4785508011
 
1.0%
565250751
 
1.0%
21966015831
 
1.0%
2500000001
 
1.0%
2330204721
 
1.0%
87451021
 
1.0%
9989999421
 
1.0%
6452221
 
1.0%
Other values (89)89
89.0%
ValueCountFrequency (%)
6452221
1.0%
11045901
1.0%
12888621
1.0%
20000001
1.0%
20366451
1.0%
29963311
1.0%
38214721
1.0%
61681541
1.0%
65014271
1.0%
78014571
1.0%
ValueCountFrequency (%)
2.509983876 × 10121
1.0%
7.56097561 × 10111
1.0%
3.251902154 × 10111
1.0%
2.100875155 × 10111
1.0%
1.832535346 × 10111
1.0%
1.126484307 × 10111
1.0%
6.574819248 × 10101
1.0%
3.873914485 × 10101
1.0%
3.139614617 × 10101
1.0%
2.871332523 × 10101
1.0%

id
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
bitcoin
 
1
0x
 
1
kyber-network
 
1
monacoin
 
1
poet
 
1
Other values (95)
95 

Length

Max length21
Median length15
Mean length7.68
Min length2

Characters and Unicode

Total characters768
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowbitcoin
2nd rowripple
3rd rowethereum
4th rowbitcoin-cash
5th rowcardano

Common Values

ValueCountFrequency (%)
bitcoin1
 
1.0%
0x1
 
1.0%
kyber-network1
 
1.0%
monacoin1
 
1.0%
poet1
 
1.0%
aelf1
 
1.0%
aeternity1
 
1.0%
factom1
 
1.0%
nxt1
 
1.0%
byteball1
 
1.0%
Other values (90)90
90.0%

Length

2022-10-23T16:31:30.590636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bitcoin1
 
1.0%
eos1
 
1.0%
ethereum1
 
1.0%
bitcoin-cash1
 
1.0%
cardano1
 
1.0%
litecoin1
 
1.0%
nem1
 
1.0%
stellar1
 
1.0%
tron1
 
1.0%
iota1
 
1.0%
Other values (90)90
90.0%

Most occurring characters

ValueCountFrequency (%)
e79
 
10.3%
n72
 
9.4%
i68
 
8.9%
o67
 
8.7%
t62
 
8.1%
a57
 
7.4%
s46
 
6.0%
c45
 
5.9%
r43
 
5.6%
m23
 
3.0%
Other values (18)206
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter746
97.1%
Dash Punctuation21
 
2.7%
Decimal Number1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e79
10.6%
n72
 
9.7%
i68
 
9.1%
o67
 
9.0%
t62
 
8.3%
a57
 
7.6%
s46
 
6.2%
c45
 
6.0%
r43
 
5.8%
m23
 
3.1%
Other values (16)184
24.7%
Dash Punctuation
ValueCountFrequency (%)
-21
100.0%
Decimal Number
ValueCountFrequency (%)
01
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin746
97.1%
Common22
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e79
10.6%
n72
 
9.7%
i68
 
9.1%
o67
 
9.0%
t62
 
8.3%
a57
 
7.6%
s46
 
6.2%
c45
 
6.0%
r43
 
5.8%
m23
 
3.1%
Other values (16)184
24.7%
Common
ValueCountFrequency (%)
-21
95.5%
01
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e79
 
10.3%
n72
 
9.4%
i68
 
8.9%
o67
 
8.7%
t62
 
8.1%
a57
 
7.4%
s46
 
6.0%
c45
 
5.9%
r43
 
5.6%
m23
 
3.0%
Other values (18)206
26.8%

last_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515230650
Minimum1515230641
Maximum1515230661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:30.726691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1515230641
5-th percentile1515230642
Q11515230646
median1515230651
Q31515230654
95-th percentile1515230658
Maximum1515230661
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.135920245
Coefficient of variation (CV)3.389530331 × 10-9
Kurtosis-1.046313324
Mean1515230650
Median Absolute Deviation (MAD)4
Skewness-0.0958501686
Sum1.51523065 × 1011
Variance26.37767677
MonotonicityNot monotonic
2022-10-23T16:31:30.866856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
151523065110
 
10.0%
15152306539
 
9.0%
15152306439
 
9.0%
15152306528
 
8.0%
15152306467
 
7.0%
15152306566
 
6.0%
15152306496
 
6.0%
15152306556
 
6.0%
15152306476
 
6.0%
15152306585
 
5.0%
Other values (9)28
28.0%
ValueCountFrequency (%)
15152306415
5.0%
15152306424
4.0%
15152306439
9.0%
15152306442
 
2.0%
15152306454
4.0%
15152306467
7.0%
15152306476
6.0%
15152306483
 
3.0%
15152306496
6.0%
15152306501
 
1.0%
ValueCountFrequency (%)
15152306611
 
1.0%
15152306585
5.0%
15152306573
 
3.0%
15152306566
6.0%
15152306556
6.0%
15152306545
5.0%
15152306539
9.0%
15152306528
8.0%
151523065110
10.0%
15152306501
 
1.0%

market_cap_usd
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7608242773
Minimum299515469
Maximum2.849090521 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:31.009953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum299515469
5-th percentile334035775.1
Q1461369198
median729732100
Q31980319073
95-th percentile1.704115229 × 1010
Maximum2.849090521 × 1011
Range2.846095366 × 1011
Interquartile range (IQR)1518949875

Descriptive statistics

Standard deviation3.234454724 × 1010
Coefficient of variation (CV)4.251250677
Kurtosis57.1483534
Mean7608242773
Median Absolute Deviation (MAD)343876120.5
Skewness7.169302609
Sum7.608242773 × 1011
Variance1.046169736 × 1021
MonotonicityStrictly decreasing
2022-10-23T16:31:31.158436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.849090521 × 10111
 
1.0%
5614788691
 
1.0%
4638965911
 
1.0%
4689139331
 
1.0%
4901035491
 
1.0%
5013300001
 
1.0%
5088864181
 
1.0%
5269011411
 
1.0%
5338815531
 
1.0%
5340761021
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
2995154691
1.0%
3087072841
1.0%
3141802541
1.0%
3171358531
1.0%
3291031481
1.0%
3342953871
1.0%
3369970191
1.0%
3537940001
1.0%
3606652461
1.0%
3814798941
1.0%
ValueCountFrequency (%)
2.849090521 × 10111
1.0%
1.192077091 × 10111
1.0%
1.001154991 × 10111
1.0%
4.442406166 × 10101
1.0%
2.591664786 × 10101
1.0%
1.657402094 × 10101
1.0%
1.481337 × 10101
1.0%
1.263463073 × 10101
1.0%
1.174164095 × 10101
1.0%
1.114385958 × 10101
1.0%

max_supply
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)66.7%
Missing73
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean3.111139815 × 1011
Minimum18900000
Maximum8 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:31.329969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18900000
5-th percentile21000000
Q168626123
median888000000
Q31.007778525 × 1010
95-th percentile1.59129 × 1011
Maximum8 × 1012
Range7.9999811 × 1012
Interquartile range (IQR)1.000915913 × 1010

Descriptive statistics

Standard deviation1.537153391 × 1012
Coefficient of variation (CV)4.9408046
Kurtosis26.95952382
Mean3.111139815 × 1011
Median Absolute Deviation (MAD)867000000
Skewness5.190586409
Sum8.400077501 × 1012
Variance2.362840548 × 1024
MonotonicityNot monotonic
2022-10-23T16:31:31.506718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
210000004
 
4.0%
1000000003
 
3.0%
10000000003
 
3.0%
840000002
 
2.0%
2.1 × 10102
 
2.0%
280000001
 
1.0%
8 × 10121
 
1.0%
532522461
 
1.0%
9989994951
 
1.0%
36005705021
 
1.0%
Other values (8)8
 
8.0%
(Missing)73
73.0%
ValueCountFrequency (%)
189000001
 
1.0%
210000004
4.0%
280000001
 
1.0%
532522461
 
1.0%
840000002
2.0%
1000000003
3.0%
1332482901
 
1.0%
8880000001
 
1.0%
9989994951
 
1.0%
10000000003
3.0%
ValueCountFrequency (%)
8 × 10121
 
1.0%
1.8447 × 10111
 
1.0%
1 × 10111
 
1.0%
4.5 × 10101
 
1.0%
2.1 × 10102
2.0%
1.6555 × 10101
 
1.0%
36005705021
 
1.0%
27795302831
 
1.0%
10000000003
3.0%
9989994951
 
1.0%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Bitcoin
 
1
0x
 
1
Kyber Network
 
1
MonaCoin
 
1
Po.et
 
1
Other values (95)
95 

Length

Max length23
Median length17
Mean length7.58
Min length2

Characters and Unicode

Total characters758
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowBitcoin
2nd rowRipple
3rd rowEthereum
4th rowBitcoin Cash
5th rowCardano

Common Values

ValueCountFrequency (%)
Bitcoin1
 
1.0%
0x1
 
1.0%
Kyber Network1
 
1.0%
MonaCoin1
 
1.0%
Po.et1
 
1.0%
aelf1
 
1.0%
Aeternity1
 
1.0%
Factom1
 
1.0%
Nxt1
 
1.0%
Byteball Bytes1
 
1.0%
Other values (90)90
90.0%

Length

2022-10-23T16:31:31.671649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
network4
 
3.4%
bitcoin3
 
2.5%
token3
 
2.5%
coin2
 
1.7%
ethereum2
 
1.7%
eos1
 
0.8%
cash1
 
0.8%
cardano1
 
0.8%
litecoin1
 
0.8%
nem1
 
0.8%
Other values (100)100
84.0%

Most occurring characters

ValueCountFrequency (%)
e69
 
9.1%
i64
 
8.4%
n58
 
7.7%
o57
 
7.5%
t53
 
7.0%
a45
 
5.9%
r35
 
4.6%
s30
 
4.0%
c25
 
3.3%
m19
 
2.5%
Other values (41)303
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter567
74.8%
Uppercase Letter170
 
22.4%
Space Separator19
 
2.5%
Other Punctuation1
 
0.1%
Decimal Number1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e69
12.2%
i64
11.3%
n58
10.2%
o57
10.1%
t53
9.3%
a45
7.9%
r35
 
6.2%
s30
 
5.3%
c25
 
4.4%
m19
 
3.4%
Other values (15)112
19.8%
Uppercase Letter
ValueCountFrequency (%)
C18
 
10.6%
B16
 
9.4%
S16
 
9.4%
N12
 
7.1%
A12
 
7.1%
D11
 
6.5%
E10
 
5.9%
T9
 
5.3%
O8
 
4.7%
R7
 
4.1%
Other values (13)51
30.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%
Decimal Number
ValueCountFrequency (%)
01
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin737
97.2%
Common21
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e69
 
9.4%
i64
 
8.7%
n58
 
7.9%
o57
 
7.7%
t53
 
7.2%
a45
 
6.1%
r35
 
4.7%
s30
 
4.1%
c25
 
3.4%
m19
 
2.6%
Other values (38)282
38.3%
Common
ValueCountFrequency (%)
19
90.5%
.1
 
4.8%
01
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e69
 
9.1%
i64
 
8.4%
n58
 
7.7%
o57
 
7.5%
t53
 
7.0%
a45
 
5.9%
r35
 
4.6%
s30
 
4.0%
c25
 
3.3%
m19
 
2.5%
Other values (41)303
40.0%

percent_change_1h
Real number (ℝ)

HIGH CORRELATION

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0517
Minimum-8.85
Maximum6.94
Zeros1
Zeros (%)1.0%
Negative40
Negative (%)40.0%
Memory size928.0 B
2022-10-23T16:31:31.845903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-8.85
5-th percentile-4.1235
Q1-1.0075
median0.15
Q31.27
95-th percentile4.3335
Maximum6.94
Range15.79
Interquartile range (IQR)2.2775

Descriptive statistics

Standard deviation2.59240735
Coefficient of variation (CV)50.14327563
Kurtosis2.219858727
Mean0.0517
Median Absolute Deviation (MAD)1.135
Skewness-0.4492577833
Sum5.17
Variance6.720575869
MonotonicityNot monotonic
2022-10-23T16:31:32.016451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.153
 
3.0%
1.272
 
2.0%
0.472
 
2.0%
0.872
 
2.0%
1.582
 
2.0%
0.122
 
2.0%
0.831
 
1.0%
2.841
 
1.0%
0.81
 
1.0%
-0.571
 
1.0%
Other values (83)83
83.0%
ValueCountFrequency (%)
-8.851
1.0%
-7.371
1.0%
-71
1.0%
-6.451
1.0%
-4.191
1.0%
-4.121
1.0%
-3.561
1.0%
-3.481
1.0%
-3.031
1.0%
-2.881
1.0%
ValueCountFrequency (%)
6.941
1.0%
6.571
1.0%
5.91
1.0%
4.921
1.0%
4.591
1.0%
4.321
1.0%
4.11
1.0%
3.421
1.0%
3.411
1.0%
2.91
1.0%

percent_change_24h
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.1692
Minimum-20.83
Maximum210.41
Zeros0
Zeros (%)0.0%
Negative49
Negative (%)49.0%
Memory size928.0 B
2022-10-23T16:31:32.182338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-20.83
5-th percentile-10.8005
Q1-3.085
median0.39
Q311.6625
95-th percentile57.3865
Maximum210.41
Range231.24
Interquartile range (IQR)14.7475

Descriptive statistics

Standard deviation34.08813558
Coefficient of variation (CV)3.051976469
Kurtosis16.71534847
Mean11.1692
Median Absolute Deviation (MAD)4.755
Skewness3.785579103
Sum1116.92
Variance1162.000987
MonotonicityNot monotonic
2022-10-23T16:31:32.345189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.761
 
1.0%
-2.851
 
1.0%
-0.441
 
1.0%
-8.921
 
1.0%
20.351
 
1.0%
15.881
 
1.0%
3.291
 
1.0%
-3.291
 
1.0%
-7.821
 
1.0%
-3.471
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
-20.831
1.0%
-15.811
1.0%
-12.631
1.0%
-12.191
1.0%
-11.951
1.0%
-10.741
1.0%
-9.231
1.0%
-8.921
1.0%
-7.821
1.0%
-7.791
1.0%
ValueCountFrequency (%)
210.411
1.0%
168.771
1.0%
132.131
1.0%
92.481
1.0%
80.881
1.0%
56.151
1.0%
53.61
1.0%
50.41
1.0%
48.131
1.0%
37.441
1.0%

percent_change_7d
Real number (ℝ)

HIGH CORRELATION

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.1208
Minimum-16.09
Maximum2099.78
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)9.0%
Memory size928.0 B
2022-10-23T16:31:32.494440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-16.09
5-th percentile-1.1955
Q116.1725
median53.495
Q3124.275
95-th percentile360.8575
Maximum2099.78
Range2115.87
Interquartile range (IQR)108.1025

Descriptive statistics

Standard deviation228.1752902
Coefficient of variation (CV)2.035084393
Kurtosis58.92819032
Mean112.1208
Median Absolute Deviation (MAD)43.29
Skewness6.928875507
Sum11212.08
Variance52063.96307
MonotonicityNot monotonic
2022-10-23T16:31:32.641967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.042
 
2.0%
6.262
 
2.0%
66.011
 
1.0%
53.291
 
1.0%
-16.091
 
1.0%
244.391
 
1.0%
122.281
 
1.0%
77.061
 
1.0%
45.411
 
1.0%
4.521
 
1.0%
Other values (88)88
88.0%
ValueCountFrequency (%)
-16.091
1.0%
-12.781
1.0%
-11.231
1.0%
-8.571
1.0%
-2.441
1.0%
-1.131
1.0%
-0.981
1.0%
-0.831
1.0%
-0.491
1.0%
2.471
1.0%
ValueCountFrequency (%)
2099.781
1.0%
482.761
1.0%
434.361
1.0%
398.451
1.0%
366.131
1.0%
360.581
1.0%
305.571
1.0%
304.981
1.0%
295.281
1.0%
294.611
1.0%

price_btc
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0160525192
Minimum1 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:32.897371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-8
5-th percentile9.725 × 10-7
Q14.35225 × 10-5
median0.00024617
Q30.0015477975
95-th percentile0.037274455
Maximum1
Range0.99999999
Interquartile range (IQR)0.001504275

Descriptive statistics

Standard deviation0.1013186856
Coefficient of variation (CV)6.311700013
Kurtosis92.41077993
Mean0.0160525192
Median Absolute Deviation (MAD)0.00024081
Skewness9.469399909
Sum1.60525192
Variance0.01026547606
MonotonicityNot monotonic
2022-10-23T16:31:33.045740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
1.0%
7.092 × 10-51
 
1.0%
0.000209061
 
1.0%
0.000501461
 
1.0%
1.349 × 10-51
 
1.0%
0.000121221
 
1.0%
0.000132011
 
1.0%
0.00364211
 
1.0%
3.23 × 10-51
 
1.0%
0.05003591
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
1 × 10-81
1.0%
8 × 10-81
1.0%
9 × 10-81
1.0%
2.4 × 10-71
1.0%
8.3 × 10-71
1.0%
9.8 × 10-71
1.0%
1.32 × 10-61
1.0%
4.4 × 10-61
1.0%
5.18 × 10-61
1.0%
5.54 × 10-61
1.0%
ValueCountFrequency (%)
11
1.0%
0.1589341
1.0%
0.07317041
1.0%
0.06251691
1.0%
0.05003591
1.0%
0.03660281
1.0%
0.02575471
1.0%
0.02425831
1.0%
0.02365951
1.0%
0.02294561
1.0%

price_usd
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.864235
Minimum0.000230495
Maximum16973.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:33.186188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.000230495
5-th percentile0.01604143
Q10.7200285
median4.072385
Q325.605125
95-th percentile616.62815
Maximum16973.8
Range16973.79977
Interquartile range (IQR)24.8850965

Descriptive statistics

Standard deviation1718.394444
Coefficient of variation (CV)6.367625721
Kurtosis92.77058087
Mean269.864235
Median Absolute Deviation (MAD)3.9837294
Skewness9.493964959
Sum26986.4235
Variance2952879.466
MonotonicityNot monotonic
2022-10-23T16:31:33.337730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16973.81
 
1.0%
1.173291
 
1.0%
3.458491
 
1.0%
8.295681
 
1.0%
0.2231191
 
1.0%
2.005321
 
1.0%
2.183871
 
1.0%
60.2511
 
1.0%
0.5344161
 
1.0%
827.741
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
0.0002304951
1.0%
0.00127651
1.0%
0.001468931
1.0%
0.004033721
1.0%
0.01373351
1.0%
0.01616291
1.0%
0.0218071
1.0%
0.072721
1.0%
0.08567691
1.0%
0.09163431
1.0%
ValueCountFrequency (%)
16973.81
1.0%
2629.231
1.0%
1210.451
1.0%
1034.211
1.0%
827.741
1.0%
605.5171
1.0%
426.0581
1.0%
401.3021
1.0%
391.3971
1.0%
379.5861
1.0%

rank
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:33.486098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.01149198
Coefficient of variation (CV)0.5744849896
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.6666667
MonotonicityStrictly increasing
2022-10-23T16:31:33.636492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
1.0%
641
 
1.0%
741
 
1.0%
731
 
1.0%
721
 
1.0%
711
 
1.0%
701
 
1.0%
691
 
1.0%
681
 
1.0%
671
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
11
1.0%
21
1.0%
31
1.0%
41
1.0%
51
1.0%
61
1.0%
71
1.0%
81
1.0%
91
1.0%
101
1.0%
ValueCountFrequency (%)
1001
1.0%
991
1.0%
981
1.0%
971
1.0%
961
1.0%
951
1.0%
941
1.0%
931
1.0%
921
1.0%
911
1.0%

symbol
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
BTC
 
1
ZRX
 
1
KNC
 
1
MONA
 
1
POE
 
1
Other values (95)
95 

Length

Max length5
Median length3
Mean length3.3
Min length2

Characters and Unicode

Total characters330
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowBTC
2nd rowXRP
3rd rowETH
4th rowBCH
5th rowADA

Common Values

ValueCountFrequency (%)
BTC1
 
1.0%
ZRX1
 
1.0%
KNC1
 
1.0%
MONA1
 
1.0%
POE1
 
1.0%
ELF1
 
1.0%
AE1
 
1.0%
FCT1
 
1.0%
NXT1
 
1.0%
GBYTE1
 
1.0%
Other values (90)90
90.0%

Length

2022-10-23T16:31:33.780458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
btc1
 
1.0%
eos1
 
1.0%
eth1
 
1.0%
bch1
 
1.0%
ada1
 
1.0%
ltc1
 
1.0%
xem1
 
1.0%
xlm1
 
1.0%
trx1
 
1.0%
miota1
 
1.0%
Other values (90)90
90.0%

Most occurring characters

ValueCountFrequency (%)
T30
 
9.1%
N27
 
8.2%
E24
 
7.3%
S24
 
7.3%
C23
 
7.0%
A21
 
6.4%
R20
 
6.1%
D19
 
5.8%
B17
 
5.2%
X17
 
5.2%
Other values (16)108
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter330
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T30
 
9.1%
N27
 
8.2%
E24
 
7.3%
S24
 
7.3%
C23
 
7.0%
A21
 
6.4%
R20
 
6.1%
D19
 
5.8%
B17
 
5.2%
X17
 
5.2%
Other values (16)108
32.7%

Most occurring scripts

ValueCountFrequency (%)
Latin330
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T30
 
9.1%
N27
 
8.2%
E24
 
7.3%
S24
 
7.3%
C23
 
7.0%
A21
 
6.4%
R20
 
6.1%
D19
 
5.8%
B17
 
5.2%
X17
 
5.2%
Other values (16)108
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T30
 
9.1%
N27
 
8.2%
E24
 
7.3%
S24
 
7.3%
C23
 
7.0%
A21
 
6.4%
R20
 
6.1%
D19
 
5.8%
B17
 
5.2%
X17
 
5.2%
Other values (16)108
32.7%

total_supply
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.640678786 × 1011
Minimum1000000
Maximum1 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-10-23T16:31:33.934691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile6766429.25
Q182348855.5
median268349874
Q32039180000
95-th percentile1.161786859 × 1011
Maximum1 × 1013
Range9.999999 × 1012
Interquartile range (IQR)1956831144

Descriptive statistics

Standard deviation1.066075585 × 1012
Coefficient of variation (CV)6.497771494
Kurtosis75.66935546
Mean1.640678786 × 1011
Median Absolute Deviation (MAD)260988432
Skewness8.424381787
Sum1.640678786 × 1013
Variance1.136517153 × 1024
MonotonicityNot monotonic
2022-10-23T16:31:34.097095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000007
 
7.0%
1000000005
 
5.0%
1 × 10112
 
2.0%
167852251
 
1.0%
552847151
 
1.0%
31415926531
 
1.0%
2600000001
 
1.0%
2736858301
 
1.0%
87451021
 
1.0%
9989999421
 
1.0%
Other values (79)79
79.0%
ValueCountFrequency (%)
10000001
1.0%
12888621
1.0%
20000001
1.0%
29963311
1.0%
38214721
1.0%
69214271
1.0%
78014571
1.0%
87451021
1.0%
94464971
1.0%
100000001
1.0%
ValueCountFrequency (%)
1 × 10131
1.0%
3.415497327 × 10121
1.0%
1.841395638 × 10121
1.0%
2.451192831 × 10111
1.0%
1.832535346 × 10111
1.0%
1.126484307 × 10111
1.0%
1.03570549 × 10111
1.0%
1 × 10112
2.0%
9.999309388 × 10101
1.0%
3.139614617 × 10101
1.0%

Interactions

2022-10-23T16:31:26.118443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:04.164056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.940435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:07.743776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:09.485438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:11.393345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:13.263012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.140491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.030006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:18.812639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:20.716220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:22.480894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.391211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:26.283727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:04.375240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.069150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:07.876485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:09.625012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:11.528240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:13.395074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.278383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.162369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:18.952451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:20.849977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:22.618713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.526146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:26.462092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:04.505201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.199717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:08.008337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:09.759978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:11.660002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:13.538362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.413624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.299700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:19.096523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:20.983972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:22.755407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.662740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:26.611715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:04.640921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.345919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:08.140776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:09.900167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:11.785242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:13.799913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.537614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.438192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:22.919836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.794855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:04.772462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.483060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:08.279695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:10.040610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:11.925948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:13.944572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.676682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.572648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:19.388106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:23.060381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.934470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:27.043146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:04.898352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.602044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:08.410483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:10.174386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:12.073143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:14.078917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.809156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.711927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:19.521680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:21.417085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:23.193696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:25.055974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:27.219028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.031764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:12.220032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:15.946740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.840081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:19.658617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:21.557548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:23.338052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:25.186785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:27.397436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.172662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:06.853685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:08.669822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:10.539981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:12.368926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:14.341971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:16.084866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:17.978862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:21.696160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:25.317725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:27.569623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.310011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:12.662866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:14.606163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:21.964388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:25.580566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:27.912307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.563850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:28.089060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.689331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:25.846367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:28.255756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:05.820703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:07.609494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-23T16:31:13.115607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:15.002496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:16.889725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:18.672507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:20.581650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:22.350754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:24.260366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-23T16:31:25.979976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-23T16:31:34.248472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-23T16:31:34.423401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-23T16:31:34.603874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-23T16:31:34.796475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-23T16:31:35.107525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-23T16:31:28.508983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-23T16:31:28.787928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-23T16:31:29.033505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 024h_volume_usdavailable_supplyidlast_updatedmarket_cap_usdmax_supplynamepercent_change_1hpercent_change_24hpercent_change_7dprice_btcprice_usdranksymboltotal_supply
002208130000016785225bitcoin15152306612849090521052.100000e+07Bitcoin-0.425.7626.041.00000016973.8000001BTC16785225
11522137000038739144847ripple15152306411192077091321.000000e+11Ripple-0.26-9.2324.150.0001863.0771902XRP99993093880
22570569000096803840ethereum1515230649100115499075NaNEthereum0.29-1.0445.010.0625171034.2100003ETH96803840
33156990000016896225bitcoin-cash1515230652444240616572.100000e+07Bitcoin Cash0.037.992.810.1589342629.2300004BCH16896225
4442830500025927070538cardano1515230654259166478564.500000e+10Cardano0.39-5.8764.990.0000600.9995985ADA31112483745
55210524000054637708litecoin1515230641165740209428.400000e+07Litecoin2.3122.2632.850.018337303.3440006LTC54637708
661460390008999999999nem151523064414813369998NaNNEM-1.82-2.5369.650.0000991.6459307XEM8999999999
7765638900017877794558stellar151523064312634630726NaNStellar1.58-4.94110.280.0000430.7067228XLM103570548975
88297161000065748192475tron151523065411741640953NaNTRON-1.80-12.63434.360.0000110.1785859TRX100000000000
991940390002779530283iota1515230652111438595822.779530e+09IOTA-3.030.8919.450.0002424.00926010MIOTA2779530283

Last rows

Unnamed: 024h_volume_usdavailable_supplyidlast_updatedmarket_cap_usdmax_supplynamepercent_change_1hpercent_change_24hpercent_change_7dprice_btcprice_usdranksymboltotal_supply
909041468300342699966civic1515230652381479894NaNCivic-1.86-6.0335.070.0000671.11316091CVC1000000000
9191704762001607622325time-new-bank1515230657360665246NaNTime New Bank3.4219.68179.280.0000140.22434792TNB5541877892
9292106852002000000digixdao1515230646353794000NaNDigixDAO0.65-12.1915.650.010693176.89700093DGD2000000
93931859950040510000gxshares1515230651336997019100000000.0GXShares-1.2311.6165.190.0005038.31886094GXS100000000
94944422920024898178walton1515230653334295387100000000.0Walton6.9432.6926.040.00081213.42650095WTC70000000
959533186200617314171quantstamp1515230657329103148NaNQuantstamp1.908.96136.700.0000320.53312196QSP976442388
96965100610050148936raiden-network-token1515230656317135853NaNRaiden Network Token1.272.2959.570.0003826.32388097RDN100000000
97971125860064355352gamecredits1515230643314180254NaNGameCredits0.072.9519.360.0002954.88196098GAME64355352
989828523900756192535enjin-coin1515230656308707284NaNEnjin Coin0.87-0.61170.820.0000250.40823999ENJ1000000000
99991415390040772871bancor1515230651299515469NaNBancor0.56-1.5948.060.0004447.345950100BNT79384422